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Data Mining

HealthScience&Technology
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Laatst bijgewerkt op 03 jul 2025 15:35:03 CESTAlle herzieningen bekijkenAlle herzieningen bekijken

Specificaties

Objectstaat
Nieuw: Een nieuw, ongelezen en ongebruikt boek in perfecte staat waarin geen bladzijden ontbreken of ...
Binding
Paperback
Edition
5
ISBN
9780443158889

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Product Identifiers

Publisher
Elsevier Science & Technology
ISBN-10
0443158886
ISBN-13
9780443158889
eBay Product ID (ePID)
4070482750

Product Key Features

Publication Name
Data Mining : Practical Machine Learning Tools and Techniques
Language
English
Subject
Intelligence (Ai) & Semantics
Publication Year
2025
Type
Textbook
Subject Area
Computers
Author
James Foulds, Christopher J. Pal, Ian H. Witten, Eibe Frank, Mark A. Hall
Format
Trade Paperback

Dimensions

Item Length
9.2 in
Item Width
7.5 in

Additional Product Features

Edition Number
5
Intended Audience
College Audience
Dewey Edition
22
Dewey Decimal
006.3
Table Of Content
PART I: INTRODUCTION TO DATA MINING 1. What's it all about? 2. Input: concepts, instances, attributes 3. Output: knowledge representation 4. Algorithms: the basic methods 5. Credibility: evaluating what's been learned 6. Preparation: data preprocessing and exploratory data analysis 7. Ethics: what are the impacts of what's been learned? PART II: MORE ADVANCED MACHINE LEARNING SCHEMES 8. Ensemble learning 9. Extending instance-based and linear models 10. Deep learning: fundamentals 11. Advanced deep learning methods 12. Beyond supervised and unsupervised learning 13. Probabilistic methods: fundamentals 14. Advanced probabilistic methods 15. Moving on: applications and their consequences
Synopsis
Data Mining: Practical Machine Learning Tools and Techniques, fifth edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, and evaluating results to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative Al (GANs, VAEs, diffusion models), large language models {transformers, BERT and GPT models}, and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Mali, and Christopher J. Pal, along with new author James R. Foulds, include today's techniques coupled with the methods at the leading edge of contemporary research. Key features, Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects, Features in-depth information on deep learning and probabilistic models, Covers performance improvement techniques, including input preprocessing and combining output from different methods, Provides an appendix introducing the WEKA machine learning workbench which implements many of the algorithms, Includes all-new exercises for each chapter, Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today's techniques coupled with the methods at the leading edge of contemporary research

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